One area of automotive vehicle technology that is evolving rapidly is the area of automotive safety applications, such as vehicle borne active safety systems. Currently most active safety systems, for example brake assist, traction control, driver assistance, and adaptive cruise control systems, comprises computers for handling information input from numerous sensors and vehicle systems in order to assess risk and provide safety enhancing support to vehicle drivers.
However, in many ways computers today are nothing more than very fast number-crunchers which have the ability to process lots of data, although usually restricted to perform a number of pre-programmed instructions in response to certain input signals.
In automotive safety applications, however, the number of input signals available in a vehicle grows larger, faster and more diverse by the day. Thus, this traditional type of computing model is inadequate to process and make sense of the volumes of information that the automotive safety applications of tomorrow will need to deal with.
Also, a lot of input data in automotive safety applications now comes in unstructured forms such as video, images, symbols and similar. Thus, a new computing model is needed in order for automotive safety applications to process and make sense of it. Such a new computing model is provided by cognitive computing systems, such as developed by IBM and others under US DARPA SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) project.
Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks in response to certain input signals, cognitive computing systems can process unstructured data and learn by experience, much in the same way humans do.
When utilizing such new cognitive computing systems in automotive safety applications, such as vehicle borne active safety systems, there comes the need for new ways of programming these cognitive computing systems to enable the development of new sensory-based cognitive computing applications.
A previous document, GB 2495265 relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego intention as well as environmental influences during machine operation. The artificial memory system comprises a Hierarchical Temporal Memory (HTM), which is a biomimetic model based on the memory prediction theory of the brain neocortex, it combines and extends the approaches used in Bayesian networks, spatial and temporal clustering algorithms and uses a hierarchy of nodes like a neural network. The HTM is trained so that it can classify, predict and/or filter signals coming from sensor inputs.
A specific deployment of this system according to GB 2495265 is in the automotive field. In this case it is assumed that the “tool” is the car and the “human” is the device. This system has two operation modes: “active” and “learning”. In “active” mode the car is in use while in “learning” mode the car may be parked in a garage or in an appropriate place with the engine turned off. In the “active-mode” a module senses the environment nearby the car using a set of sensors, such as cameras, Lidars, temperature/humidity sensors, accelerometers, etc. The module also senses dynamic response of the car to human actions, like lateral acceleration, frontal acceleration, speed, etc. Further, the module also senses “raw” driver commands, e.g. the position of steering wheel, etc. The signals generated are collected by the Artificial Memory System, which is equipped with memory patterns describing the behavior of the car. By exploiting this memory this module can classify, predict or filter the sensors data coming from the car and then send back the computation results. A database stores “raw” unexpected input pattern that may be used for improving the system performance. In the learning mode, a Pattern Recorder feeds the previously stored patterns in the database to the artificial memory system to train it over them. By this combination of signals the artificial memory system is learning the new patterns provided by the Pattern Recorder module.
However, cognitive computing systems get better over time as they build knowledge and learn a domain, its processes and its methods of interacting. Thus, although GB 2495265 proposes one method of having an automotive system learn from its experiences, there is room for further improvement. This in particular as this new type of neural network computers operating in accordance with such new computing models, as provided by cognitive computing systems, e.g. such as those mentioned above developed by IBM and others under US DARPA SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) project, require new method of programming to optimize their use in vehicles.